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Krzywanski J, Sosnowski M, Grabowska K, Zylka A, Lasek L, Kijo-Kleczkowska A. Advanced Computational Methods for Modeling, Prediction and Optimization-A Review. MATERIALS (BASEL, SWITZERLAND) 2024; 17:3521. [PMID: 39063813 PMCID: PMC11279266 DOI: 10.3390/ma17143521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/28/2024]
Abstract
This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions.
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Affiliation(s)
- Jaroslaw Krzywanski
- Department of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland; (M.S.); (K.G.); (A.Z.)
| | - Marcin Sosnowski
- Department of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland; (M.S.); (K.G.); (A.Z.)
| | - Karolina Grabowska
- Department of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland; (M.S.); (K.G.); (A.Z.)
| | - Anna Zylka
- Department of Advanced Computational Methods, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland; (M.S.); (K.G.); (A.Z.)
| | - Lukasz Lasek
- Wladyslaw Bieganski Collegium Medicum, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland;
| | - Agnieszka Kijo-Kleczkowska
- Department of Thermal Machinery, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 42-201 Czestochowa, Poland;
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Bonatti AF, Vozzi G, De Maria C. Enhancing quality control in bioprinting through machine learning. Biofabrication 2024; 16:022001. [PMID: 38262061 DOI: 10.1088/1758-5090/ad2189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/23/2024] [Indexed: 01/25/2024]
Abstract
Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.
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Affiliation(s)
- Amedeo Franco Bonatti
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Giovanni Vozzi
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Carmelo De Maria
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
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Cuahuizo-Huitzil G, Olivares-Xometl O, Eugenia Castro M, Arellanes-Lozada P, Meléndez-Bustamante FJ, Pineda Torres IH, Santacruz-Vázquez C, Santacruz-Vázquez V. Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5720. [PMID: 37630012 PMCID: PMC10456520 DOI: 10.3390/ma16165720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/15/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions. In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.
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Affiliation(s)
- Guadalupe Cuahuizo-Huitzil
- Facultad de Ingeniería Química, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Puebla 72570, Mexico; (G.C.-H.); (O.O.-X.); (P.A.-L.)
| | - Octavio Olivares-Xometl
- Facultad de Ingeniería Química, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Puebla 72570, Mexico; (G.C.-H.); (O.O.-X.); (P.A.-L.)
| | - María Eugenia Castro
- Centro de Química, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Puebla 72570, Mexico;
| | - Paulina Arellanes-Lozada
- Facultad de Ingeniería Química, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Puebla 72570, Mexico; (G.C.-H.); (O.O.-X.); (P.A.-L.)
| | - Francisco J. Meléndez-Bustamante
- Laboratoria de Química Teórica, Centro de Investigación, Deptartamento de Fisicoquímica, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma, Av. San Claudio y 18 Sur, Puebla 72570, Mexico;
| | - Ivo Humberto Pineda Torres
- Facultad de Ciencias de la Computación, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 14 Sur, Puebla 72570, Mexico;
| | - Claudia Santacruz-Vázquez
- Facultad de Ingeniería Química, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Puebla 72570, Mexico; (G.C.-H.); (O.O.-X.); (P.A.-L.)
| | - Verónica Santacruz-Vázquez
- Facultad de Ingeniería Química, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Puebla 72570, Mexico; (G.C.-H.); (O.O.-X.); (P.A.-L.)
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Ma M, Zhou H, Gao S, Li N, Guo W, Dai Z. Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network. Polymers (Basel) 2023; 15:2813. [PMID: 37447459 DOI: 10.3390/polym15132813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
Electrospinning technology enables the fabrication of electrospun nanofibers with exceptional properties, which are highly influenced by their diameter. This work focuses on the electrospinning of polyacrylonitrile (PAN) to obtain PAN nanofibers under different processing conditions. The morphology and size of the resulting PAN nanofibers were characterized using scanning electron microscopy (SEM), and the corresponding diameter data were measured using Nano Measure 1.2 software. The processing conditions and corresponding nanofiber diameter data were then inputted into an artificial neural network (ANN) to establish the relationship between the electrospinning process parameters (polymer concentration, applied voltage, collecting distance, and solution flow rate), and the diameter of PAN nanofibers. The results indicate that the polymer concentration has the greatest influence on the diameter of PAN nanofibers. The developed neural network prediction model provides guidance for the preparation of PAN nanofibers with specific dimensions.
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Affiliation(s)
- Ming Ma
- School of Life Sciences, Tiangong University, Tianjin 300387, China
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China
| | - Huchen Zhou
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Suhan Gao
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Nan Li
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China
- School of Chemistry, Tiangong University, Tianjin 300387, China
| | - Wenjuan Guo
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China
- School of Pharmaceutical Sciences, Tiangong University, Tianjin 300387, China
| | - Zhao Dai
- State Key Laboratory of Separation Membranes and Membrane Processes, Tiangong University, Tianjin 300387, China
- School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
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Rohim MAS, Nazmi N, Bahiuddin I, Mazlan SA, Norhaniza R, Yamamoto S, Nordin NA, Abdul Aziz SA. Prediction for magnetostriction magnetorheological foam using machine learning method. J Appl Polym Sci 2022. [DOI: 10.1002/app.52798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Muhamad Amirul Sunni Rohim
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Nurhazimah Nazmi
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Irfan Bahiuddin
- Mechanical Engineering Department, Vocational College Universitas Gadjah Mada Yogyakarta Indonesia
| | - Saiful Amri Mazlan
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Rizuan Norhaniza
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
| | - Shin‐Ichiroh Yamamoto
- Department of Bio‐Science and Engineering Shibaura Institute of Technology Saitama Japan
| | - Nur Azmah Nordin
- Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia
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Xie Y, Chen J, Zhao H, Huang F. Prediction of the fiber diameter of melt electrospinning writing by kriging model. J Appl Polym Sci 2022. [DOI: 10.1002/app.52212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Yu Xie
- School of Mechanical Engineering and Automation Fuzhou University Fuzhou China
| | - Jianxiong Chen
- School of Mechanical Engineering and Automation Fuzhou University Fuzhou China
| | - Han Zhao
- School of Mechanical Engineering and Automation Fuzhou University Fuzhou China
| | - Feng Huang
- School of Mechanical Engineering and Automation Fuzhou University Fuzhou China
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Maurya AK, Nagamani M, Kang SW, Yeom JT, Hong JK, Sung H, Park CH, Uma Maheshwera Reddy P, Reddy NS. Development of artificial neural networks software for arsenic adsorption from an aqueous environment. ENVIRONMENTAL RESEARCH 2022; 203:111846. [PMID: 34364860 DOI: 10.1016/j.envres.2021.111846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/26/2021] [Accepted: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Arsenic contamination is a global problem, as it affects the health of millions of people. For this study, data-driven artificial neural network (ANN) software was developed to predict and validate the removal of As(V) from an aqueous solution using graphene oxide (GO) under various experimental conditions. A reliable model for wastewater treatment is essential in order to predict its overall performance and to provide an idea of how to control its operation. This model considered the adsorption process parameters (initial concentration, adsorbent dosage, pH, and residence time) as the input variables and arsenic removal as the only output. The ANN model predicted the adsorption efficiency with high accuracy for both training and testing datasets, when compared with the available response surface methodology (RSM) model. Based on the best model synaptic weights, user-friendly ANN software was created to predict and analyze arsenic removal as a function of adsorption process parameters. We developed various graphical user interfaces (GUI) for easy use of the developed model. Thus, a researcher can efficiently operate the software without an understanding of programming or artificial neural networks. Sensitivity analysis and quantitative estimation were carried out to study the function of adsorption process parameter variables on As(V) removal efficiency, using the GUI of the model. The model prediction shows that the adsorbent dosages, initial concentration, and pH are the most influential parameters. The efficiency was increased as the adsorbent dosages increased, decreasing with initial concentration and pH. The result show that the pH 2.0-5.0 is optimal for adsorbent efficiency (%).
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Affiliation(s)
- A K Maurya
- Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, 51508, South Korea; School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - M Nagamani
- School of Computer and Information Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India
| | - Seung Won Kang
- Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, 51508, South Korea
| | - Jong-Taek Yeom
- Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, 51508, South Korea
| | - Jae-Keun Hong
- Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, 51508, South Korea
| | - Hyokyung Sung
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - C H Park
- Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, 51508, South Korea.
| | | | - N S Reddy
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, 52828, Republic of Korea.
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